{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "catboost美国油田 延安油田.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "JdHjKK59QPpw",
        "RxmATbIoV0IC",
        "0-RdrH4MT_i7"
      ],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOurZcYkqPci6WQ4E2TcRDo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunyingjian/AI-in-well-logging/blob/master/catboost%E7%BE%8E%E5%9B%BD%E6%B2%B9%E7%94%B0_%E5%BB%B6%E5%AE%89%E6%B2%B9%E7%94%B0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bb8TA1HvLCSo"
      },
      "source": [
        "#延安油田"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cx1T14UgJud7"
      },
      "source": [
        "##引入数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8UWD6B68-Au8",
        "outputId": "dafbbfd3-4735-450e-9d63-39dea2226bc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "!git clone https://github.com/sunyingjian/numpy-.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'numpy-'...\n",
            "remote: Enumerating objects: 3, done.\u001b[K\n",
            "remote: Counting objects: 100% (3/3), done.\u001b[K\n",
            "remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
            "remote: Total 327 (delta 0), reused 0 (delta 0), pack-reused 324\u001b[K\n",
            "Receiving objects: 100% (327/327), 181.81 MiB | 31.18 MiB/s, done.\n",
            "Resolving deltas: 100% (80/80), done.\n",
            "Checking out files: 100% (162/162), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYxNrHPaAwSm",
        "outputId": "3978dbd4-9eed-4ad5-e0e7-e4e5f1490ab2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/39/128fff65072c8327371e3c594f3c826d29c85b21cb6485980353b168e0e4/catboost-0.24.2-cp36-none-manylinux1_x86_64.whl (66.1MB)\n",
            "\u001b[K     |████████████████████████████████| 66.2MB 108kB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.1.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.6/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from catboost) (1.18.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.24.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_-yQBIDBtLT"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import catboost as cb\n",
        "from catboost import CatBoostClassifier, Pool\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9nfdpn3B700"
      },
      "source": [
        "train = pd.read_csv('/content/numpy-/3345train_data.csv')\n",
        "test = pd.read_csv('/content/numpy-/3345test_data.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yj1V-XzzCbJi",
        "outputId": "844a27a2-5ae7-498a-f164-6b8e3f54eb64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "train"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AC</th>\n",
              "      <th>CAL</th>\n",
              "      <th>GR</th>\n",
              "      <th>K</th>\n",
              "      <th>RD</th>\n",
              "      <th>SP</th>\n",
              "      <th>Core Lithology</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.029102</td>\n",
              "      <td>0.031789</td>\n",
              "      <td>0.026724</td>\n",
              "      <td>0.202335</td>\n",
              "      <td>0.078986</td>\n",
              "      <td>0.333754</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.032883</td>\n",
              "      <td>0.033469</td>\n",
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              "      <td>0.203141</td>\n",
              "      <td>0.076064</td>\n",
              "      <td>0.333669</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.034385</td>\n",
              "      <td>0.037006</td>\n",
              "      <td>0.032359</td>\n",
              "      <td>0.200121</td>\n",
              "      <td>0.074503</td>\n",
              "      <td>0.333619</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.243816</td>\n",
              "      <td>0.143381</td>\n",
              "      <td>0.086581</td>\n",
              "      <td>0.290115</td>\n",
              "      <td>0.017649</td>\n",
              "      <td>0.094025</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.248043</td>\n",
              "      <td>0.146786</td>\n",
              "      <td>0.084643</td>\n",
              "      <td>0.283874</td>\n",
              "      <td>0.017286</td>\n",
              "      <td>0.090554</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3340</th>\n",
              "      <td>0.012092</td>\n",
              "      <td>0.067866</td>\n",
              "      <td>0.007657</td>\n",
              "      <td>0.077713</td>\n",
              "      <td>0.181798</td>\n",
              "      <td>0.348797</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3341</th>\n",
              "      <td>0.011831</td>\n",
              "      <td>0.069989</td>\n",
              "      <td>0.007606</td>\n",
              "      <td>0.081135</td>\n",
              "      <td>0.206839</td>\n",
              "      <td>0.348560</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3342</th>\n",
              "      <td>0.011533</td>\n",
              "      <td>0.070121</td>\n",
              "      <td>0.009591</td>\n",
              "      <td>0.081337</td>\n",
              "      <td>0.250115</td>\n",
              "      <td>0.348470</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3343</th>\n",
              "      <td>0.008903</td>\n",
              "      <td>0.069325</td>\n",
              "      <td>0.010596</td>\n",
              "      <td>0.081135</td>\n",
              "      <td>0.281774</td>\n",
              "      <td>0.348340</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3344</th>\n",
              "      <td>0.003230</td>\n",
              "      <td>0.070210</td>\n",
              "      <td>0.012674</td>\n",
              "      <td>0.080129</td>\n",
              "      <td>0.329088</td>\n",
              "      <td>0.348228</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3345 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            AC       CAL        GR  ...        RD        SP  Core Lithology\n",
              "0     0.029102  0.031789  0.026724  ...  0.078986  0.333754               5\n",
              "1     0.032883  0.033469  0.030592  ...  0.076064  0.333669               5\n",
              "2     0.034385  0.037006  0.032359  ...  0.074503  0.333619               5\n",
              "3     0.243816  0.143381  0.086581  ...  0.017649  0.094025               3\n",
              "4     0.248043  0.146786  0.084643  ...  0.017286  0.090554               3\n",
              "...        ...       ...       ...  ...       ...       ...             ...\n",
              "3340  0.012092  0.067866  0.007657  ...  0.181798  0.348797               5\n",
              "3341  0.011831  0.069989  0.007606  ...  0.206839  0.348560               5\n",
              "3342  0.011533  0.070121  0.009591  ...  0.250115  0.348470               5\n",
              "3343  0.008903  0.069325  0.010596  ...  0.281774  0.348340               5\n",
              "3344  0.003230  0.070210  0.012674  ...  0.329088  0.348228               5\n",
              "\n",
              "[3345 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q4YUsa9VCVHp"
      },
      "source": [
        "train_data = train.drop(columns=['Core Lithology'])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9oFMTxrCmTs"
      },
      "source": [
        "train_label = train['Core Lithology'].values"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n56k3NFnDk_y"
      },
      "source": [
        "test_data = test.drop(columns=['Core Lithology'])\n",
        "test_label = test['Core Lithology'].values"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qDgZtPcEJp1H"
      },
      "source": [
        "##使用catboost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WVGtoZZFjE-"
      },
      "source": [
        "train_pool = Pool(train_data,train_label)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InTGbpLyGGT8"
      },
      "source": [
        "test_pool = Pool(test_data)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toPn-bRGZ13W"
      },
      "source": [
        "from sklearn import metrics"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xloFEhse-9QL",
        "outputId": "27181a00-c2f5-4105-9f13-a1c529d193ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "model = CatBoostClassifier(\n",
        "                           learning_rate=0.01,\n",
        "                           l2_leaf_reg=3,\n",
        "                           n_estimators = 7000,\n",
        "                           loss_function='MultiClass',\n",
        "                           verbose=True)\n",
        "# train the model\n",
        "model.fit(train_pool)\n",
        "# make the prediction using the resulting model\n",
        "preds_class = model.predict(test_pool, prediction_type='Class')\n",
        "#print(accuracy_score(test_label,preds_class))\n",
        "print('confusion matrix')\n",
        "print(metrics.confusion_matrix(test_label, preds_class))\n",
        "print('classification report')\n",
        "print(metrics.classification_report(test_label, preds_class))\n",
        "print('Accuracy : %f' % (metrics.accuracy_score(test_label, preds_class)))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "6999:\tlearn: 0.1008269\ttotal: 1m 24s\tremaining: 0us\n",
            "confusion matrix\n",
            "[[19  1  0  0  0  0  0]\n",
            " [ 0 73  4  0  0  0  3]\n",
            " [ 0  1 74  2  0  0  3]\n",
            " [ 0  3  2 14  0  0  1]\n",
            " [ 0  0  1  0 51  0  1]\n",
            " [ 0  0  0  0  0 13  0]\n",
            " [ 0  6  9  0  1  0 84]]\n",
            "classification report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       1.00      0.95      0.97        20\n",
            "           2       0.87      0.91      0.89        80\n",
            "           3       0.82      0.93      0.87        80\n",
            "           4       0.88      0.70      0.78        20\n",
            "           5       0.98      0.96      0.97        53\n",
            "           6       1.00      1.00      1.00        13\n",
            "           7       0.91      0.84      0.87       100\n",
            "\n",
            "    accuracy                           0.90       366\n",
            "   macro avg       0.92      0.90      0.91       366\n",
            "weighted avg       0.90      0.90      0.90       366\n",
            "\n",
            "Accuracy : 0.896175\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Za3g01i1XzQj"
      },
      "source": [
        "a = pd.DataFrame(preds_class)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eFNQO-EmXdrH"
      },
      "source": [
        "a.to_csv('./aaa.csv')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPXByTTZIqHw"
      },
      "source": [
        "##加入smote"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MZYF7wHlKeEG",
        "outputId": "c8b4d973-ab77-48c9-8b66-bded5e8432dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(Counter(train_label))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counter({7: 928, 3: 751, 2: 710, 5: 482, 1: 194, 4: 166, 6: 114})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SDNY9T_IpfT",
        "outputId": "1d33bf68-344e-4fcb-fcc6-83b08d403106",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "from collections import Counter\n",
        "sm = SMOTE(random_state=123)\n",
        "X_train_sm, y_train_sm = sm.fit_sample(train_data,train_label)\n",
        "\n",
        "train_data_sm = pd.DataFrame(X_train_sm)\n",
        "train_label_sm = pd.DataFrame(y_train_sm)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.neighbors.base module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.neighbors. Anything that cannot be imported from sklearn.neighbors is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R-zvNd2oJynh"
      },
      "source": [
        "train_pool_sm = Pool(train_data_sm,train_label_sm)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4rqMIB-ZJ7Cu",
        "outputId": "e2a0a17e-5c4f-44b7-8e35-67a40b95d40e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "model = CatBoostClassifier(\n",
        "                           learning_rate=0.01,\n",
        "                           l2_leaf_reg=3,\n",
        "                           n_estimators = 8000,\n",
        "                           loss_function='MultiClass',\n",
        "                           verbose=True)\n",
        "# train the model\n",
        "model.fit(train_pool_sm)\n",
        "# make the prediction using the resulting model\n",
        "preds_class = model.predict(test_pool, prediction_type='Class')\n",
        "print('confusion matrix')\n",
        "print(metrics.confusion_matrix(test_label, preds_class))\n",
        "print('classification report')\n",
        "print(metrics.classification_report(test_label, preds_class))\n",
        "print('Accuracy : %f' % (metrics.accuracy_score(test_label, preds_class)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "CatBoostError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mCatBoostError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-30-123b53856277>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_pool_sm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# make the prediction using the resulting model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mpreds_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_pool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Class'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'confusion matrix'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfusion_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_label\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds_class\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, data, prediction_type, ntree_start, ntree_end, thread_count, verbose)\u001b[0m\n\u001b[1;32m   4350\u001b[0m                   \u001b[0;32mwith\u001b[0m \u001b[0mlog\u001b[0m \u001b[0mprobability\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mevery\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0meach\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4351\u001b[0m         \"\"\"\n\u001b[0;32m-> 4352\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'predict'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4353\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4354\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_start\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_end\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_predict\u001b[0;34m(self, data, prediction_type, ntree_start, ntree_end, thread_count, verbose, parent_method_name)\u001b[0m\n\u001b[1;32m   1977\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_prediction_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprediction_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1978\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1979\u001b[0;31m         \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1980\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata_is_single_object\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/catboost/core.py\u001b[0m in \u001b[0;36m_base_predict\u001b[0;34m(self, pool, prediction_type, ntree_start, ntree_end, thread_count, verbose)\u001b[0m\n\u001b[1;32m   1308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_base_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1310\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_base_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_start\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1312\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_base_virtual_ensembles_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprediction_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mntree_end\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvirtual_ensembles_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthread_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._base_predict\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m_catboost.pyx\u001b[0m in \u001b[0;36m_catboost._CatBoost._base_predict\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mCatBoostError\u001b[0m: catboost/libs/data/model_dataset_compatibility.cpp:81: At position 0 should be feature with name 0 (found AC)."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w1WK84JVQEMy"
      },
      "source": [
        "#美国油田"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JdHjKK59QPpw"
      },
      "source": [
        "##引入数据"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gH-hk5qwQfRD",
        "outputId": "45c9b78b-82c7-4531-df8a-7584e25d9874",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "! git clone https://github.com/seg/tutorials-2016.git"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "fatal: destination path 'tutorials-2016' already exists and is not an empty directory.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zW07Ha1AQNw8",
        "outputId": "7a841d6e-dd04-4577-a40f-4050c237fb04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "%matplotlib inline\n",
        "#%matplotlib inline 可以在Ipython编译器里直接使用，功能是可以内嵌绘图，并且可以省略掉plt.show()这一步。\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.colors as colors\n",
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "from pandas import set_option\n",
        "set_option(\"display.max_rows\", 10)#设置要显示的默认行数，显示的最大行数是10\n",
        "pd.options.mode.chained_assignment = None #为了在增加列表行数的时候防止出现setting with copy warning\n",
        "filename = 'facies_vectors.csv'\n",
        "training_data_us = pd.read_csv('/content/tutorials-2016/1610_Facies_classification/training_data.csv')\n",
        "training_data_us"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.0</td>\n",
              "      <td>77.450</td>\n",
              "      <td>0.664</td>\n",
              "      <td>9.900</td>\n",
              "      <td>11.915</td>\n",
              "      <td>4.600</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2793.5</td>\n",
              "      <td>78.260</td>\n",
              "      <td>0.661</td>\n",
              "      <td>14.200</td>\n",
              "      <td>12.565</td>\n",
              "      <td>4.100</td>\n",
              "      <td>1</td>\n",
              "      <td>0.979</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.0</td>\n",
              "      <td>79.050</td>\n",
              "      <td>0.658</td>\n",
              "      <td>14.800</td>\n",
              "      <td>13.050</td>\n",
              "      <td>3.600</td>\n",
              "      <td>1</td>\n",
              "      <td>0.957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2794.5</td>\n",
              "      <td>86.100</td>\n",
              "      <td>0.655</td>\n",
              "      <td>13.900</td>\n",
              "      <td>13.115</td>\n",
              "      <td>3.500</td>\n",
              "      <td>1</td>\n",
              "      <td>0.936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHRIMPLIN</td>\n",
              "      <td>2795.0</td>\n",
              "      <td>74.580</td>\n",
              "      <td>0.647</td>\n",
              "      <td>13.500</td>\n",
              "      <td>13.300</td>\n",
              "      <td>3.400</td>\n",
              "      <td>1</td>\n",
              "      <td>0.915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3227</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3120.5</td>\n",
              "      <td>46.719</td>\n",
              "      <td>0.947</td>\n",
              "      <td>1.828</td>\n",
              "      <td>7.254</td>\n",
              "      <td>3.617</td>\n",
              "      <td>2</td>\n",
              "      <td>0.685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3228</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3121.0</td>\n",
              "      <td>44.563</td>\n",
              "      <td>0.953</td>\n",
              "      <td>2.241</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.344</td>\n",
              "      <td>2</td>\n",
              "      <td>0.677</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3229</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3121.5</td>\n",
              "      <td>49.719</td>\n",
              "      <td>0.964</td>\n",
              "      <td>2.925</td>\n",
              "      <td>8.013</td>\n",
              "      <td>3.190</td>\n",
              "      <td>2</td>\n",
              "      <td>0.669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3122.0</td>\n",
              "      <td>51.469</td>\n",
              "      <td>0.965</td>\n",
              "      <td>3.083</td>\n",
              "      <td>7.708</td>\n",
              "      <td>3.152</td>\n",
              "      <td>2</td>\n",
              "      <td>0.661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3231</th>\n",
              "      <td>5</td>\n",
              "      <td>C LM</td>\n",
              "      <td>CHURCHMAN BIBLE</td>\n",
              "      <td>3122.5</td>\n",
              "      <td>50.031</td>\n",
              "      <td>0.970</td>\n",
              "      <td>2.609</td>\n",
              "      <td>6.668</td>\n",
              "      <td>3.295</td>\n",
              "      <td>2</td>\n",
              "      <td>0.653</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3232 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Facies Formation        Well Name   Depth  ...   PHIND     PE  NM_M  RELPOS\n",
              "0          3     A1 SH        SHRIMPLIN  2793.0  ...  11.915  4.600     1   1.000\n",
              "1          3     A1 SH        SHRIMPLIN  2793.5  ...  12.565  4.100     1   0.979\n",
              "2          3     A1 SH        SHRIMPLIN  2794.0  ...  13.050  3.600     1   0.957\n",
              "3          3     A1 SH        SHRIMPLIN  2794.5  ...  13.115  3.500     1   0.936\n",
              "4          3     A1 SH        SHRIMPLIN  2795.0  ...  13.300  3.400     1   0.915\n",
              "...      ...       ...              ...     ...  ...     ...    ...   ...     ...\n",
              "3227       5      C LM  CHURCHMAN BIBLE  3120.5  ...   7.254  3.617     2   0.685\n",
              "3228       5      C LM  CHURCHMAN BIBLE  3121.0  ...   8.013  3.344     2   0.677\n",
              "3229       5      C LM  CHURCHMAN BIBLE  3121.5  ...   8.013  3.190     2   0.669\n",
              "3230       5      C LM  CHURCHMAN BIBLE  3122.0  ...   7.708  3.152     2   0.661\n",
              "3231       5      C LM  CHURCHMAN BIBLE  3122.5  ...   6.668  3.295     2   0.653\n",
              "\n",
              "[3232 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9YAhsd-kotmi"
      },
      "source": [
        "blind_data = training_data_us[training_data_us['Well Name'] == 'SHANKLE']\n",
        "training_data_us = training_data_us[training_data_us['Well Name'] != 'SHANKLE']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3lap3zdTGCn",
        "outputId": "4e11edfe-f6d2-4b28-cfe5-74a57a0101b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "training_data_us['Well Name'] = training_data_us['Well Name'].astype('category')\n",
        "training_data_us['Formation'] = training_data_us['Formation'].astype('category')\n",
        "training_data_us['Well Name'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SHRIMPLIN', 'LUKE G U', 'CROSS H CATTLE', 'NOLAN', 'Recruit F9', 'NEWBY', 'CHURCHMAN BIBLE']\n",
              "Categories (7, object): ['SHRIMPLIN', 'LUKE G U', 'CROSS H CATTLE', 'NOLAN', 'Recruit F9', 'NEWBY',\n",
              "                         'CHURCHMAN BIBLE']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0kgN2GvoTTEr",
        "outputId": "4590bb47-ad58-4872-889f-8fbdc30b25fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "correct_facies_labels = training_data_us['Facies'].values\n",
        "\n",
        "feature_vectors = training_data_us.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)\n",
        "feature_vectors.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "      <td>2783.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>66.249445</td>\n",
              "      <td>0.644637</td>\n",
              "      <td>3.754963</td>\n",
              "      <td>13.118929</td>\n",
              "      <td>3.805693</td>\n",
              "      <td>1.523895</td>\n",
              "      <td>0.523057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>31.610849</td>\n",
              "      <td>0.241922</td>\n",
              "      <td>5.045916</td>\n",
              "      <td>7.389665</td>\n",
              "      <td>0.894118</td>\n",
              "      <td>0.499518</td>\n",
              "      <td>0.287499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>13.250000</td>\n",
              "      <td>-0.025949</td>\n",
              "      <td>-21.832000</td>\n",
              "      <td>0.550000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>46.081500</td>\n",
              "      <td>0.497000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>8.165000</td>\n",
              "      <td>3.200000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.276500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>65.541000</td>\n",
              "      <td>0.627000</td>\n",
              "      <td>3.581000</td>\n",
              "      <td>11.900000</td>\n",
              "      <td>3.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.529000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>80.714000</td>\n",
              "      <td>0.812323</td>\n",
              "      <td>6.500000</td>\n",
              "      <td>16.144000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.771500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>361.150000</td>\n",
              "      <td>1.480000</td>\n",
              "      <td>18.500000</td>\n",
              "      <td>84.400000</td>\n",
              "      <td>8.094000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                GR    ILD_log10  ...         NM_M       RELPOS\n",
              "count  2783.000000  2783.000000  ...  2783.000000  2783.000000\n",
              "mean     66.249445     0.644637  ...     1.523895     0.523057\n",
              "std      31.610849     0.241922  ...     0.499518     0.287499\n",
              "min      13.250000    -0.025949  ...     1.000000     0.010000\n",
              "25%      46.081500     0.497000  ...     1.000000     0.276500\n",
              "50%      65.541000     0.627000  ...     2.000000     0.529000\n",
              "75%      80.714000     0.812323  ...     2.000000     0.771500\n",
              "max     361.150000     1.480000  ...     2.000000     1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQ22QpAwThCr"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "scaler = preprocessing.StandardScaler().fit(feature_vectors)\n",
        "scaled_features = scaler.transform(feature_vectors)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j0bxAeYBTnBq"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "        scaled_features, correct_facies_labels, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxmATbIoV0IC"
      },
      "source": [
        "##使用smote"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MqsZkZqV3as",
        "outputId": "5b55e0ac-2c20-44ca-e49e-d8edddcabd38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "from collections import Counter\n",
        "print(Counter(y_train))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Counter({2: 519, 3: 414, 8: 370, 6: 319, 4: 144, 5: 138, 1: 131, 9: 127, 7: 64})\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nLxmqRqjV7zV",
        "outputId": "78f37035-8326-4542-e3dd-2524ee3a14a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "sm = SMOTE(random_state=123,sampling_strategy={\n",
        "                                              1:400, 5:400,4:300,9:300,7:300\n",
        "})\n",
        "X_train_sm, y_train_sm = sm.fit_sample(X_train,y_train)\n",
        "\n",
        "X_train = pd.DataFrame(X_train_sm)\n",
        "y_train = pd.DataFrame(y_train_sm)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-RdrH4MT_i7"
      },
      "source": [
        "##使用catboost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aWgDi-wToGD"
      },
      "source": [
        "train_pool_us = Pool(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H2RfMbwOTxXt"
      },
      "source": [
        "test_pool_us = Pool(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kWavnIh3UFPx",
        "outputId": "00cdb6ad-aac0-40a5-f220-cc39c9e918e4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "model = CatBoostClassifier(\n",
        "                           learning_rate=0.1,\n",
        "                           l2_leaf_reg=3,\n",
        "                           n_estimators = 3000,\n",
        "                           loss_function='MultiClass',\n",
        "                           verbose=True)\n",
        "# train the model\n",
        "model.fit(train_pool_us)\n",
        "#make the prediction using the resulting model\n",
        "preds_class = model.predict(test_pool_us, prediction_type='Class')\n",
        "print(accuracy_score(y_test,preds_class))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "2015:\tlearn: 0.0666877\ttotal: 32.2s\tremaining: 15.7s\n",
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            "2019:\tlearn: 0.0665895\ttotal: 32.3s\tremaining: 15.7s\n",
            "2020:\tlearn: 0.0665142\ttotal: 32.3s\tremaining: 15.7s\n",
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            "0.7127468581687613\n"
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          "name": "stdout"
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BWeqGf3AUQSJ"
      },
      "source": [
        "##引入盲井"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJWB6gOkVK_d",
        "outputId": "4b9a0e37-ee53-40b5-c653-89c7a11e594e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "blind_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Facies</th>\n",
              "      <th>Formation</th>\n",
              "      <th>Well Name</th>\n",
              "      <th>Depth</th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>471</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2774.5</td>\n",
              "      <td>98.36</td>\n",
              "      <td>0.642</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>18.685</td>\n",
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              "    <tr>\n",
              "      <th>472</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
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              "      <td>97.57</td>\n",
              "      <td>0.631</td>\n",
              "      <td>7.9</td>\n",
              "      <td>16.745</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2775.5</td>\n",
              "      <td>98.41</td>\n",
              "      <td>0.615</td>\n",
              "      <td>12.8</td>\n",
              "      <td>14.105</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2776.0</td>\n",
              "      <td>85.92</td>\n",
              "      <td>0.597</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.385</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>2</td>\n",
              "      <td>A1 SH</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>2776.5</td>\n",
              "      <td>83.16</td>\n",
              "      <td>0.592</td>\n",
              "      <td>12.3</td>\n",
              "      <td>13.345</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3006.0</td>\n",
              "      <td>32.84</td>\n",
              "      <td>1.120</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>3.455</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3006.5</td>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3007.0</td>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>918</th>\n",
              "      <td>4</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3007.5</td>\n",
              "      <td>26.22</td>\n",
              "      <td>1.092</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>3.400</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>919</th>\n",
              "      <td>4</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3008.0</td>\n",
              "      <td>65.36</td>\n",
              "      <td>1.026</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.715</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>449 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Facies Formation Well Name   Depth  ...   PHIND   PE  NM_M  RELPOS\n",
              "471       2     A1 SH   SHANKLE  2774.5  ...  18.685  2.9     1   1.000\n",
              "472       2     A1 SH   SHANKLE  2775.0  ...  16.745  3.2     1   0.984\n",
              "473       2     A1 SH   SHANKLE  2775.5  ...  14.105  3.2     1   0.968\n",
              "474       2     A1 SH   SHANKLE  2776.0  ...  13.385  3.4     1   0.952\n",
              "475       2     A1 SH   SHANKLE  2776.5  ...  13.345  3.4     1   0.935\n",
              "..      ...       ...       ...     ...  ...     ...  ...   ...     ...\n",
              "915       8      C LM   SHANKLE  3006.0  ...   3.455  5.1     2   0.060\n",
              "916       8      C LM   SHANKLE  3006.5  ...   2.890  4.8     2   0.045\n",
              "917       8      C LM   SHANKLE  3007.0  ...   2.890  4.8     2   0.030\n",
              "918       4      C LM   SHANKLE  3007.5  ...   3.400  4.5     2   0.030\n",
              "919       4      C LM   SHANKLE  3008.0  ...   4.715  4.5     2   0.015\n",
              "\n",
              "[449 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aKQBvh1eVg6-"
      },
      "source": [
        "blind_data['Well Name'] = blind_data['Well Name'].astype('category')\n",
        "blind_data['Formation'] = blind_data['Formation'].astype('category')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKWh0CkvVp12",
        "outputId": "4f2460fe-21c5-4ad1-843f-6c95fd5e3a7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "blind_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3006.0</td>\n",
              "      <td>32.84</td>\n",
              "      <td>1.120</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>3.455</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>916</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3006.5</td>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>917</th>\n",
              "      <td>8</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3007.0</td>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>918</th>\n",
              "      <td>4</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3007.5</td>\n",
              "      <td>26.22</td>\n",
              "      <td>1.092</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>3.400</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>919</th>\n",
              "      <td>4</td>\n",
              "      <td>C LM</td>\n",
              "      <td>SHANKLE</td>\n",
              "      <td>3008.0</td>\n",
              "      <td>65.36</td>\n",
              "      <td>1.026</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.715</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>449 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Facies Formation Well Name   Depth  ...   PHIND   PE  NM_M  RELPOS\n",
              "471       2     A1 SH   SHANKLE  2774.5  ...  18.685  2.9     1   1.000\n",
              "472       2     A1 SH   SHANKLE  2775.0  ...  16.745  3.2     1   0.984\n",
              "473       2     A1 SH   SHANKLE  2775.5  ...  14.105  3.2     1   0.968\n",
              "474       2     A1 SH   SHANKLE  2776.0  ...  13.385  3.4     1   0.952\n",
              "475       2     A1 SH   SHANKLE  2776.5  ...  13.345  3.4     1   0.935\n",
              "..      ...       ...       ...     ...  ...     ...  ...   ...     ...\n",
              "915       8      C LM   SHANKLE  3006.0  ...   3.455  5.1     2   0.060\n",
              "916       8      C LM   SHANKLE  3006.5  ...   2.890  4.8     2   0.045\n",
              "917       8      C LM   SHANKLE  3007.0  ...   2.890  4.8     2   0.030\n",
              "918       4      C LM   SHANKLE  3007.5  ...   3.400  4.5     2   0.030\n",
              "919       4      C LM   SHANKLE  3008.0  ...   4.715  4.5     2   0.015\n",
              "\n",
              "[449 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAn5RBx8XixD"
      },
      "source": [
        "blind_label = blind_data['Facies']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsyiYNonW2Ki"
      },
      "source": [
        "blind_data = blind_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTmbyeJT8O7_"
      },
      "source": [
        "blind_data.reset_index(drop=True, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYY08aoq8vLr",
        "outputId": "8200f931-4129-4c4d-fe79-9b68b96e04b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "blind_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>GR</th>\n",
              "      <th>ILD_log10</th>\n",
              "      <th>DeltaPHI</th>\n",
              "      <th>PHIND</th>\n",
              "      <th>PE</th>\n",
              "      <th>NM_M</th>\n",
              "      <th>RELPOS</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>98.36</td>\n",
              "      <td>0.642</td>\n",
              "      <td>-0.1</td>\n",
              "      <td>18.685</td>\n",
              "      <td>2.9</td>\n",
              "      <td>1</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>97.57</td>\n",
              "      <td>0.631</td>\n",
              "      <td>7.9</td>\n",
              "      <td>16.745</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>98.41</td>\n",
              "      <td>0.615</td>\n",
              "      <td>12.8</td>\n",
              "      <td>14.105</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>85.92</td>\n",
              "      <td>0.597</td>\n",
              "      <td>13.0</td>\n",
              "      <td>13.385</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>83.16</td>\n",
              "      <td>0.592</td>\n",
              "      <td>12.3</td>\n",
              "      <td>13.345</td>\n",
              "      <td>3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>444</th>\n",
              "      <td>32.84</td>\n",
              "      <td>1.120</td>\n",
              "      <td>-2.2</td>\n",
              "      <td>3.455</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445</th>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>446</th>\n",
              "      <td>25.16</td>\n",
              "      <td>1.112</td>\n",
              "      <td>-1.6</td>\n",
              "      <td>2.890</td>\n",
              "      <td>4.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>447</th>\n",
              "      <td>26.22</td>\n",
              "      <td>1.092</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>3.400</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>448</th>\n",
              "      <td>65.36</td>\n",
              "      <td>1.026</td>\n",
              "      <td>1.6</td>\n",
              "      <td>4.715</td>\n",
              "      <td>4.5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>449 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        GR  ILD_log10  DeltaPHI   PHIND   PE  NM_M  RELPOS\n",
              "0    98.36      0.642      -0.1  18.685  2.9     1   1.000\n",
              "1    97.57      0.631       7.9  16.745  3.2     1   0.984\n",
              "2    98.41      0.615      12.8  14.105  3.2     1   0.968\n",
              "3    85.92      0.597      13.0  13.385  3.4     1   0.952\n",
              "4    83.16      0.592      12.3  13.345  3.4     1   0.935\n",
              "..     ...        ...       ...     ...  ...   ...     ...\n",
              "444  32.84      1.120      -2.2   3.455  5.1     2   0.060\n",
              "445  25.16      1.112      -1.6   2.890  4.8     2   0.045\n",
              "446  25.16      1.112      -1.6   2.890  4.8     2   0.030\n",
              "447  26.22      1.092      -0.4   3.400  4.5     2   0.030\n",
              "448  65.36      1.026       1.6   4.715  4.5     2   0.015\n",
              "\n",
              "[449 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gqmd3dz2Xhg-"
      },
      "source": [
        "blind_pool = Pool(blind_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xXKxQJJ5XaoY",
        "outputId": "af33c004-f1bf-42f1-9c08-efcb6a375426",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "preds_class = model.predict(blind_pool, prediction_type='Class')\n",
        "print(accuracy_score(blind_label,preds_class))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.04899777282850779\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}
